PyTorch Essential Training: Deep Learning

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Jonathan Fernandes

56:03

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1. Introduction

001. Welcome.mp4

00:57

002. What you should know before watching this course.mp4

00:45

2. Fashion MNIST and Neural Networks

003. Working with the Fashion MNIST dataset.mp4

03:40

004. Neural network intuition.mp4

05:08

3. Working with Classes and Tensors

005. Classes.mp4

04:02

006. Tensors.mp4

03:43

007. Training the network.mp4

02:59

4. Working with Loss, Autograd, and Optimizers

008. Loss.mp4

05:00

009. Autograd.mp4

04:44

010. Autograd with tensors.mp4

04:27

011. Optimizers.mp4

01:49

012. Using optimizers.mp4

03:34

5. Troubleshooting and CPU GPU Usage

013. Troubleshooting.mp4

04:48

014. CPU to GPU.mp4

05:59

015. Validation.mp4

02:56

6. Conclusion

016. Future project ideas.mp4

01:32

Description

PyTorch is quickly becoming one of the most popular deep learning frameworks around, as well as a must-have skill in your artificial intelligence tool kit. It's gained admiration from industry leaders due to its deep integration with Python; its integration with top cloud platforms, including Amazon SageMaker and Google Cloud Platform; and its computational graphs that can be defined on the fly. In this course, join Jonathan Fernandes as he dives into the basics of deep learning using PyTorch. Starting with a working image recognition model, he shows how the different components fit and work in tandem—from tensors, loss functions, and autograd all the way to troubleshooting a PyTorch network.

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Deep Learning

PyTorch

Jonathan Fernandes

Instructor's Courses

I am an experienced Data Scientist and Machine Learning/ Artificial Intelligence consultant that leads teams to create AI products that help meet business objectives. I have led teams to create solutions for a variety of verticals including banking, healthcare, energy, telecoms and travel and transport. Outside of work I spend time with my fabulous family, swim regularly in the sea throughout the year and am actively involved in my local church.

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